An Improved Neural Network Algorithm for Classifying the Transmission Line Faults Slavko Vasilic

1. An Improved Neural Network Algorithm for Classifying the Transmission Line Faults Slavko Vasilic Dr Mladen Kezunovic Texas A&M University

2. Outline • Problem • Goal • Task • Identifying context using neural network (NN) • Algorithm implementation • Advanced processing of NN inputs/outputs • Conclusion

3. Problem • Traditional relay settings are computed ahead of time based on worst case fault conditions and related phasors • The settings may be incorrect for the unfolding events • The actual transients may cause a measurement error that can cause a significant impact on the phasor estimates

4. Goal • Design a new relaying strategy that does not have traditional relay setting • Optimize the algorithm performance in each prevailing network conditions • Improve simultaneously both, dependability and security of the relay operation

5. Task • Implement a new pattern recognition based protection algorithm • Use a neural network and apply it directly to the samples of voltage and current signals • Produce the fault type and zone classification in real time • Study various approaches for preprocessing NN inputs and fuzzyfication of NN outputs

6. Identifying Context Using NN Characteristic of the neural network • Direct use of samples (no feature extraction) • Flat structure (no hidden layers) • Self-organizing • Unsupervised and supervised learning • Outputs are prototypes of typical patterns • Adaptability for non-stationary inputs

7. Identifying Context Using NN Training steps

8. Algorithm Implementation Training and testing • Power network model is used to simulate various fault events • Fault events are determined with varying fault parameters: type, location, impedance and inception time • The simulation results are used for building the patterns for protection algorithm evaluation

9. Algorithm Implementation Simulation of scenario cases • Training tasks are recognizing the type and zone of the fault • Test patterns correspond to a new set of previously unseen scenarios • Test patterns are classified according to their similarity to established prototypes by applying nearest neighbor classifier

10. Algorithm Implementation Example of patterns for various fault parameters

11. Algorithm Implementation The outcome of training are pattern prototypes

12. Advanced Processing of NN Inputs Properties of signal processing • Data selected for training: currents, voltages or both • Sampling frequency • Moving data window length • Analog filter characteristics • Scaling ratio between voltage and current samples

13. Advanced Processing of NN Inputs Moving data window for taking the samples

14. Advanced Processing of NN Inputs Example of the patterns for various scaling ratios

15. Advanced Processing of NN Outputs Fuzzyfied classification of a test pattern

16. Advanced Processing of NN Outputs Fuzzyfied classification of a test pattern • Determine appropriate number of nearest prototypes to be taken into account • Include the weighted distances between a pattern and selected prototypes • Include the size of selected prototypes

17. prototype fuzzy class membership Advanced Processing of NN Outputs Fuzzy K-nearest neighbor classifier test pattern prototype considered number of neighboring prototypes fuzzy weight test pattern class membership weighted distance

18. Algorithm Evaluation Propagation of classif. error during testing

19. Conclusion • Protection algorithm is based on unique self­organized neural network and uses voltages and currents as inputs • Tuning of input signal preprocessing steps significantly affects algorithm behavior during training and testing • Fuzzyfication of NN outputs improves algorithm selectivity for previously unseen events

20. Conclusion • The algorithm establishes prototypes of typical patterns (events) • Proposed approach enables accurate fault type and fault location classification • The power network model is used to simulate a variety of fault and normal events